Core concepts in Windsor.ai

Explore key terms and concepts to understand the Windsor.ai platform and its functionality better.

Data refers to raw information collected from various marketing platforms. For example, in Facebook Ads, data includes impressions, clicks, conversions, cost per click (CPC), return on ad spend (ROAS), and so on.

A data source is the original location (platform, website, app) where data is generated or stored. In Windsor.ai, you select a data source from which to pull data into any destination for further analysis. Windsor.ai supports 315+ data sources, including Google Analytics 4 (GA4), Facebook Ads, Google Ads, LinkedIn Ads, etc.

A destination is a tool where data is sent for further processing. You can work with various destinations in Windsor.ai, including:

• Cloud data warehouses (e.g., Amazon Redshift, Google BigQuery)
• Business intelligence tools (e.g., Looker Studio, Tableau, Power BI)
• Spreadsheet applications (e.g., Google Sheets, Excel)
• Databases (e.g., MySQL, Azure Blob Storage)

ELT stands for Extract, Load, and Transform. An ELT connector is a software component of Windsor.ai that automates data movement from a source to a destination. 

As the name suggests, ELT first loads data in the destination before transforming it. This approach ensures that raw data is transferred as-is, giving data engineers and analysts more flexibility in data processing.

A connection refers to the link between a data source and a destination. A successful connection always results in the desired data being displayed in the desired destination. When a user clicks “test connection” in the Windsor.ai dashboard, our system verifies the permissions needed to access the data and whether the credentials provided are correct. If authentication and verification are accurate, a connection is established, and if not, Windsor.ai shows an error message.

A field in Windsor.ai is a specific data attribute or metric extracted from a data source. For example, in the GA4 data source, fields include “bounce rate,” “sessions,” and “age.” You can view all available fields in the documentation or data preview dashboard and select only those needed for your report.

A query is a curated request for specific data from a data source. Windsor.ai allows users to create custom queries, filtering, aggregating, and extracting the data they want from the source.

Dimension is a qualitative data attribute that helps categorize, segment, and analyze datasets. In Windsor.ai, dimensions describe the characteristics of users, events, or interactions. For example, in Google Analytics 4, dimensions can include attributes such as device type, country, campaign name, or landing page. They serve to group and filter data for deeper analysis.

Metric is a quantitative data point that measures performance or tracks user behavior. Unlike dimensions, which cover categories, metrics focus on numerical values, such as sessions, bounce rate, page views, conversion rate, and revenue. In Windsor.ai, metrics allow users to track the effectiveness of campaigns and evaluate business performance.

Windsor.ai provides a vast library of pre-built data visualization templates to help users quickly set up custom dashboards, reports, and analyses, removing the need to build them from scratch. You can easily customize Windsor.ai’s templates for various data sources and destinations, such as Looker Studio, Power BI, Excel, Google Sheets, and Tableau, to fit your specific business needs.

Data backfilling in Windsor.ai allows users to access both historical and recent data from a source. It’s useful for recovering lost data, setting up new connections, or working with large datasets.

A schedule in Windsor.ai defines the interval at which data is automatically extracted from the source and transferred to the destination. Users can set schedules for hourly, daily, or weekly updates, ensuring that analytics dashboards always reflect the latest insights without manual effort.

The “Auto-add all accounts” feature in Windsor.ai is designed for agencies and individuals working on multiple projects, allowing them to connect all available accounts within a data source automatically. This eliminates the need for manual setup, saving time when managing multiple accounts.

Windsor.ai’s multitouch attribution software allows users to track and analyze all interactions a potential customer has with a brand before making a purchase. Unlike marketing attribution modeling, which assigns credit, multitouch attribution aims to provide a holistic view of the entire customer journey.

For example, a customer might first see a Facebook ad, receive an email newsletter, visit the website through an organic search, and later click on a Google ad before converting. Windsor.ai helps businesses track all these touchpoints, providing insights into how different marketing efforts contribute to customer decisions. By understanding this journey, companies can refine their messaging, improve customer engagement, and invest in the most effective channels.

The Budget Optimizer is a special tool in Windsor.ai that uses machine learning to help businesses efficiently allocate their advertising budget across various channels. By analyzing past marketing performance, Windsor.ai recommends the best budget distribution for future campaigns. 

Explore more essential marketing and data-related terms in our Marketing Data Library to enhance your understanding and maximize your experience with Windsor.ai.